1. Read:
a) Hidden Markov model 
https://en.wikipedia.org/wiki/Hidden_Markov_model

b) Viterbi_algorithm
Given the model parameters and observed data, estimate the optimal sequence of hidden states.
https://en.wikipedia.org/wiki/Viterbi_algorithm

c) Baum–Welch algorithm
Given just the observed data, estimate the model parameters.
https://en.wikipedia.org/wiki/Baum%E2%80%93Welch_algorithm

d) Forward–backward algorithm
Given the model parameters and observed data, calculate the likelihood of the data.
https://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm

2) R programming:
https://cran.r-project.org/web/packages/HMM/HMM.pdf
https://cran.r-project.org/web/packages/depmixS4/vignettes/depmixS4.pdf
https://cran.r-project.org/web/packages/aphid/vignettes/aphid-vignette.html


3)Examples:
- based on stock data, but well explained (shows how to use R depmixS4 package) https://www.quantstart.com/articles/hidden-markov-models-for-regime-detection-using-r
- Python based CG-island detection example https://github.com/jmschrei/pomegranate/blob/master/tutorials/Tutorial_3_Hidden_Markov_Models.ipynb

4) Do exercises in Lab9.html and Lab9.Rmd

5) Repeat/homework
a) go to https://www.gutenberg.org/ and download "On the Origin of Species" by Charles Darwin
b) repeat the correction exercise, but this time with English text (https://en.wikipedia.org/wiki/English_phonology):
- download the whole book
- make training set from k-1 chapters
- use previously not used chapter for making the darwin.chapterX.txt and mutdarwin.chapterX.txt (introduce errors using 'mutate' function)